Only a few recent studies have addressed the RBMs' overfitting problem with Dropout-based regularization. For instance, Wang et al. [1]} have introduced a fast version of Dropout, but not aiming RBMs as their primary focus. The proposed approach is employed in classification and regression tasks and works by sampling from a Gaussian approximation instead of applying the Monte Carlo “optimization”.
